1. Field
Certain aspects of the present disclosure generally relate to neural system engineering and, more particularly, to methods and apparatus for unsupervised neural replay, learning refinement, association, and memory transfer.
2. Background
In the field of neural system engineering, there is a fundamental problem of truly replaying, in absence of an original stimulus, a neural firing pattern that has been learned by one or more neurons. Further, the problems of fast learning, learning refinement, association, and memory transfer after the original stimulus is no longer present still remain to be addressed.
Current methods of learning a pattern with biologically inspired neuron models are functionally one-way methods: in order to determine what pattern a neuron matches, one would need to try different patterns until the matching one is found. A method of true replay of what has been learned, either biologically or by machine is unknown.